Multimodal Large Language Models (MLLMs)

Researchers introduced DocHop, a benchmark for integrated chart-context reasoning in document-style images, finding that the best multimodal large language model achieves only 62.83% accuracy compared…

Researchers introduced VeriOCRBench, a 1,800-sample human-verified benchmark with 1,600 trap-injected invalid tasks across 8 trap types and 200 trap-free controls, to test whether multimodal large lan…

Researchers introduced GraphVerse, a benchmark for evaluating multimodal large language models (MLLMs) on visual graph reasoning, which tests perception, structural understanding, and multi-step reaso…